Prefabricated comprehensive pipe gallery transportation trailer early warning system

Abstract

The invention belongs to the technical field of pipe gallery safe transportation, and particularly relates to a prefabricated comprehensive pipe gallery transportation trailer early warning system which comprises the following operation steps: step 1, when the front road condition needs to be reconnaissance, a driver starts an unmanned aerial vehicle control system through a vehicle-mounted central control system and inputs a target address in a Beidou navigation system for navigation; and meanwhile, the Beidou navigation system synchronizes a navigation path to a communication module arranged in the unmanned aerial vehicle control system in a preparation state. According to the early warning system, the unmanned aerial vehicle flies at a long distance in front of the trailer, road condition changes and potential dangers at a long distance can be found in advance, compared with a traditional early warning system which only depends on the limited sensing range of the trailer, the early warning time and distance are greatly prolonged, the dynamic early warning mode is more flexible and timely, and the early warning efficiency is improved. And the method can better adapt to complex and changeable road environments.

Description

Technical Field

[0001] The present invention relates to the technical field of safe transportation of pipe corridors, and in particular to an early warning system for a prefabricated integrated pipe corridor transportation trailer. Background Art

[0002] Good road conditions are the prerequisite for safe transportation of prefabricated integrated pipe corridors, and are also the determining factors of transportation efficiency. As an important part of urban infrastructure construction, the quality and safety of prefabricated integrated pipe corridors are directly related to the normal operation and development of the city. In the transportation of large prefabricated integrated pipe corridor components, due to the large size and heavy weight of the components, high requirements are placed on transportation conditions. The unevenness of the road surface will cause the transport vehicle to bump during driving, and the complexity of the road vehicle working conditions, such as frequent lane changes and overtaking by other vehicles, will also cause the transport vehicle to be disturbed by external forces, resulting in severe shaking. This bump and shake will not only affect the stability of transportation, but may also cause cracks in the pipe corridor structure and destroy the integrity of the pipe corridor structure, thereby affecting the service life and safety of the pipe corridor. The traditional early warning system can only rely on the vehicle's own limited perception range and cannot obtain distant road condition information in advance, resulting in the vehicle responding only when it is close to the dangerous area, increasing the risk of accidents. In particular, when encountering emergencies, such as sudden congestion, construction areas or areas affected by bad weather, it is difficult for the trailer to respond in time, increasing the risk of collisions and delayed transportation.

[0003] In view of the above problems, the present invention proposes an early warning system for a prefabricated integrated pipeline corridor transport trailer. Summary of the invention

[0004] Based on the technical problem that the existing early warning timing is not timely, the present invention proposes an early warning system for a prefabricated integrated pipeline corridor transport trailer.

[0005] The present invention proposes a prefabricated integrated pipe gallery transport trailer early warning system, and the operation steps are as follows:

[0006] Step 1: When it is necessary to conduct reconnaissance of the road conditions ahead, the driver starts the UAV control system through the vehicle-mounted central control system, enters the target address in the Beidou navigation system for navigation, and at the same time, the Beidou navigation system synchronizes the navigation path to the communication module set in the UAV control system in the standby state. After receiving the navigation path, the UAV control system performs path analysis and planning, and then lets the UAV take off to perform the reconnaissance mission. The UAV flies to the front position of the trailer head with a preset distance between the two according to the pre-synchronized navigation path and real-time road condition information, and transmits the detected images and data back to the vehicle-mounted central control system through the communication module in real time. The bumpiness of the road conditions ahead is calculated through an algorithm, and then the bumpiness is classified and analyzed, and then the vehicle-mounted central control system informs the driver that he needs to reduce the speed to the specified speed to pass;

[0007] Step 2: At this time, the drone located in front of the trailer head flies to a preset distance from the trailer head, and maintains a speed synchronized with the trailer. The power management and monitoring set in the drone control system synchronously monitors the power of the drone. If the battery power is lower than the preset value, the vehicle-mounted central control system sends a warning message to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to recycle and charge the communication module of the drone control system. The drone control system dispatches this signal to its internal task execution and control module, and the task execution and control module controls the drone to fly back to the top of the trailer's charging position. Before charging, the drone feeds back the charging signal to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to the drone control system to start another drone to continue to perform the road condition monitoring task;

[0008] In step 3, after the drone that returns to charge in step 2 falls into the charging position on the roof of the trailer, the unmanned charging docking module in the vehicle control system sends a charging signal to the charging mechanism set on the top of the trailer.

[0009] Preferably, the vehicle-mounted central control includes a Beidou navigation system, a vehicle control system and a drone control system. The vehicle control system collects the vehicle's operating status information through various sensors and accurately controls various components of the vehicle according to preset control strategies. The drone control system is responsible for two-way communication with the drone and comprehensive management of its flight operations.

[0010] Preferably, the UAV control system includes a power management and monitoring module, a task execution and control module, a UAV switching management module and a data transmission and processing module. The power management and monitoring module includes power monitoring and charging scheduling. The power monitoring monitors the power of the UAV in real time. When the power is lower than a preset value, an early warning message is promptly sent to the on-board central control system to remind the system that the UAV needs to be charged or replaced to ensure that the UAV can continue to perform the road condition monitoring task. The charging scheduling receives the recovery charging signal sent by the on-board central control system and dispatches this signal to the task execution and control module to control the UAV to fly back to the top of the charging position of the trailer.

[0011] Preferably, the task execution and control module includes path planning and navigation, flight attitude control and synchronous flight control. The path planning and navigation receives the navigation path synchronized by the Beidou navigation system, and performs path analysis and planning to guide the UAV to fly to the designated position in front of the front of the trailer along a preset path, thereby ensuring that the UAV can accurately reach the reconnaissance area and provide protection for road condition monitoring. The flight attitude control accurately controls the attitude of the UAV during flight so that it can fly stably in front of the trailer without being disturbed by external airflow factors, thereby ensuring the accuracy and stability of the collected images and data. The synchronous flight control controls the UAV to maintain a speed synchronized with the trailer, so that the UAV can continuously monitor the road conditions in front of the trailer in real time.

[0012] Preferably, the vehicle control system includes a vehicle control module, a wireless charging docking module, a weather detection module, a wireless transmission module and a data processing module. The vehicle control module ensures that the vehicle can stably drive according to the driver's strategy by accurately controlling the acceleration, deceleration, steering and braking operations of the vehicle. The wireless charging docking module accurately determines the position and posture of the drone relative to the front of the trailer by using positioning technology and sensors, guides the drone to accurately land at the charging position on the top of the trailer and establishes a stable charging connection at the same time. The weather detection module collects weather data around the vehicle in real time through various meteorological sensors installed on the vehicle, analyzes and processes the collected weather data, and promptly issues warning information to the driver when it detects that severe weather conditions or weather change trends may affect the driving safety of the vehicle. The wireless transmission module overcomes interference and signal attenuation problems in wireless communication by automatically selecting the optimal communication frequency band, adjusting the transmission power and adopting error correction coding technology to ensure efficient and accurate transmission of data between the trailer and the external device. By automatically selecting the optimal communication frequency band, adjusting the transmission power, adopting error correction coding and other technical means, the interference and signal attenuation problems in wireless communication are overcome to ensure efficient and accurate transmission of data between the vehicle and the external device. At the same time, the security of communication is guaranteed to prevent data from being illegally stolen or tampered with, the data processing module.

[0013] Preferably, the steps of calculating the road bumpiness of the drone by a texture analysis algorithm based on a gray level co-occurrence matrix are as follows:

[0014] Feature extraction, first, use drones to collect road condition photos within a certain distance in front of the trailer, grayscale these photos, calculate their grayscale co-occurrence matrix, and then extract texture features such as contrast, correlation, energy, and entropy from the grayscale co-occurrence matrix. Contrast reflects the drastic change of grayscale values ​​in the image. For bumpy roads, the contrast value is usually higher; correlation reflects the linear relationship between pixels, and the correlation is higher for flat roads; energy indicates the uniformity of the grayscale distribution of the image, and the energy is lower for bumpy roads with complex textures; entropy reflects the amount of information in the image, and the entropy value of the bumpy area is generally larger;

[0015] Establish a bumpiness assessment model, collect a large number of drone photos under different road conditions and their corresponding actual bumpiness assessment data. These data are obtained through professional measurement equipment or manual field surveys. The calculated texture features are used as input and the actual bumpiness is used as output. Use machine learning algorithms to establish an assessment model.

[0016] A speed decision model is established. The input of the model is the road bumpiness, and the output is the most suitable driving speed for the trailer. The mapping relationship between different bumpiness and the most suitable speed is determined through a large amount of experimental data and actual driving experience.

[0017] Calculation and speed adjustment: calculate the texture features of the current road conditions, then input them into the trained bumpiness assessment model to obtain the bumpiness assessment value of the current road conditions, input the bumpiness assessment value into the speed decision model, and calculate the most appropriate driving speed for the trailer;

[0018] The information is displayed on the display screen of the vehicle's central control system.

[0019] Preferably, a fixing plate is provided on the top of the front of the trailer, and a limiting groove is provided on the body of the fixing plate. The charging mechanism is placed inside the limiting groove. Limiting blocks are symmetrically provided on the surface of the trailer compartment, and threaded holes are provided on the inclined surfaces of the limiting blocks. The inclined surfaces of the limiting blocks are fixedly connected to the surface of the trailer compartment by threads. Right-angle frames are fixedly connected to the opposite surfaces of the two limiting blocks, and a pipe gallery is provided between the two limiting blocks. The four corners of the lower end of the pipe gallery are in contact with the surface of the right-angle frame respectively.

[0020] Preferably, the charging mechanism includes a plurality of cylinders, and mounting grooves are respectively provided on the four sides of the fixing plate, the cylinder body of the cylinder is fixedly connected to the inner wall of the mounting groove, one end of the cylinder piston rod is fixedly connected to a push rod, and both ends of the push rod are slidably connected to the inner wall of the mounting groove, a stepping motor is arranged at the upper end of the push rod, and one end of the output shaft of the stepping motor is fixedly connected to a connecting rod, and both ends of the connecting rod are rotatably connected to pressing blocks, a sensor is arranged between the two pressing blocks, and the lower end of the sensor is fixedly connected to the upper surface of the push rod, a power supply board is provided on the inner bottom wall of the mounting groove provided on the fixing plate, and an infinite wireless charging board is attached to the upper surface of the power supply board, and the side of the wireless charging board is fixedly connected to the lower end of the drone through a connecting column, and the upper surface of the wireless charging board is tightly attached to the lower surface of the pressing block.

[0021] Preferably, a silicone gasket is provided on the lower surface of each pressing block.

[0022] The beneficial effects of the present invention are:

[0023] 1. By setting up the drone to fly at a longer distance in front of the trailer, it is possible to detect road condition changes and potential dangers at a longer distance in advance. Compared with the traditional warning system that can only rely on the limited perception range of the vehicle itself, the time and distance of the warning are greatly extended. This dynamic warning method is more flexible and timely, and can better adapt to the complex and changing road environment.

[0024] 2. By setting up multiple drones, when one drone fails, runs out of power, or needs maintenance, other drones will immediately take over its work to ensure the uninterrupted operation of road condition monitoring and early warning functions. This redundant design effectively avoids the risk of failure of the entire early warning system due to failure of a single device, and improves the stability and reliability of the system during long-term driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of an early warning system for a prefabricated integrated pipe gallery transport trailer proposed by the present invention;

[0026] Figure 2 This is a vehicle control system architecture diagram of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0027] Figure 3 This is a UAV control system architecture diagram of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0028] Figure 4 This is a UAV data processing flow chart of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0029] Figure 5 A three-dimensional diagram of a trailer of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0030] Figure 6 A cross-sectional view of a fixing plate of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0031] Figure 7 The invention proposes a prefabricated integrated pipe gallery transport trailer early warning system Figure 6 The enlarged view of point A in the middle;

[0032] Figure 8 The invention proposes a prefabricated integrated pipe gallery transport trailer early warning system Figure 7 The enlarged view of A1 in the middle;

[0033] Fig. 9 A stereoscopic diagram of a push rod of a prefabricated integrated pipe gallery transport trailer early warning system proposed by the present invention;

[0034] Fig.10 A drone stereogram of the prefabricated integrated pipeline corridor transport trailer early warning system proposed in the present invention.

[0035] In the figure: 1. trailer; 2. fixing plate; 3. charging mechanism; 31. cylinder; 32. push rod; 33. motor; 34. pressing block; 35. sensor; 36. drone; 37. wireless charging plate; 4. limit block; 5. pipe gallery; 6. right-angle bracket. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0037] Reference Figure 1-Figure 10 , a prefabricated integrated pipe gallery transport trailer early warning system, the operation steps are as follows:

[0038] Step 1: When it is necessary to conduct reconnaissance of the road conditions ahead, the driver starts the drone control system through the vehicle-mounted central control system, enters the target address in the Beidou navigation system for navigation, and at the same time, the Beidou navigation system synchronizes the navigation path to the communication module set in the drone control system in the standby state. After receiving the navigation path, the drone control system performs path analysis and planning, and then lets the DJI Air 3 drone 36 take off to perform the reconnaissance mission. The drone 36 flies to the front position of the front of the trailer 1 with a preset distance between the two according to the pre-synchronized navigation path and real-time road condition information, and transmits the detected images and data back to the vehicle-mounted central control system through the communication module in real time, calculates the bumpiness of the road conditions ahead through an algorithm, and then classifies and analyzes the bumpiness, and informs the driver through the vehicle-mounted central control system that he needs to reduce the speed to a specified speed to pass;

[0039] Step 2: At this time, the drone 36 located in front of the front of the trailer 1 flies to a preset distance from the front of the trailer 1, and maintains a speed that is synchronized with the trailer 1. The power management and monitoring set in the drone control system synchronously monitors the power of the drone 36. If the battery power is lower than the preset value, the vehicle-mounted central control system sends a warning message to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to the communication module of the drone control system to recycle and charge. The drone control system dispatches this signal to its internal task execution and control module, and the task execution and control module controls the drone 36 to fly back to the top of the charging position of the trailer 1. Before charging, the drone 36 feeds back the charging signal to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to the drone control system to start another drone 36 to continue to perform the road condition monitoring task;

[0040] In step 3 and step 2, after the drone 36 returning for charging falls into the charging position on the roof of the trailer 1, the unmanned charging docking module in the vehicle control system uses a wireless charging system based on an orthogonal magnetic coupling mechanism to send a charging signal to the charging mechanism 3 set on the top of the trailer 1.

[0041] Specifically, the battery capacity of DJI Air 3 is 4241mAh, and the voltage is generally around 15.22V. According to the formula W=UIT (W is power, U is voltage, I is current, and T is time), W 总 =15.22*4.241*3600 is about 232033.6J, and the charging efficiency of the wireless charging system based on the orthogonal magnetic coupling mechanism is 90.86%. According to the formula W 实 =W 总 / n(W 总 is the actual electrical energy required to be input, n is the charging efficiency), we can get W 实=232033.6J / 90.86% is approximately 255374.8J. The charging power of the above-mentioned power supply board is set to 500W. According to the formula t=W / P, the time t required for full charging is: t=255374.8J / 500W is approximately 510.75s, which is approximately 8.51 minutes when converted into minutes. In summary, in a windless state, the above-mentioned drone 36 and the wireless charging system based on the orthogonal magnetic coupling mechanism can fully charge the drone 36 within 10 minutes, and the effective cruising range of the DJI Air 3 drone 36 exceeds 25 minutes. Fast charging can reduce the charging time of multiple drones 36 and avoid reducing the effective monitoring time of the drone 36, thereby increasing the risk of the corridor 5 being bumped during driving, and further increasing the possibility of damage to the corridor 5.

[0042] In this embodiment, the vehicle-mounted central control includes a Beidou navigation system, a vehicle control system and a drone control system. The vehicle control system collects the vehicle's operating status information through various sensors and accurately controls various components of the vehicle according to a preset control strategy. The drone control system is responsible for two-way communication with the drone 36 and comprehensive management of its flight operations.

[0043] Specifically, based on the vehicle position information and target reconnaissance position-related information provided by the Beidou navigation system, the drone control system will plan a reasonable flight path for the drone 36. During the flight, if any emergency occurs, such as strong wind interference or obstacles, the drone control system will automatically and timely adjust the flight path of the drone 36 according to the real-time flight status of the drone 36 and new situations, to ensure that the drone 36 can complete the flight mission safely and smoothly.

[0044] In this embodiment, the UAV control system includes a power management and monitoring module, a task execution and control module, a UAV switching management module, and a data transmission and processing module. The power management and monitoring module includes power monitoring and charging scheduling. The power monitoring monitors the power of the UAV in real time. When the power is lower than the preset value, it promptly sends an early warning message to the on-board central control system to remind the system that the UAV needs to be charged or replaced to ensure that the UAV can continue to perform road condition monitoring tasks. The charging scheduling receives the recovery charging signal sent by the on-board central control system and dispatches this signal to the task execution and control module to control the UAV to fly back to the top of the charging position of the trailer 1.

[0045] Specifically, the power safety value of the drone 36 is set to 15%. When the power is lower than the preset safety power value, it will send a warning message to the vehicle-mounted central control system and start the power management mechanism. In this case, the vehicle-mounted central control system will recall the drone 36 for charging according to the actual situation and arrange another drone 36 to continue the reconnaissance mission. If it is decided to recall the drone 36, the drone control system will issue corresponding instructions to guide the drone 36 to fly back to the top of the vehicle's charging position to ensure that it can charge smoothly.

[0046] In this embodiment, the task execution and control module includes path planning and navigation, flight attitude control and synchronous flight control. The path planning and navigation receives the navigation path synchronized by the Beidou navigation system, and performs path analysis and planning to guide the UAV 36 to fly to the designated position in front of the front of the trailer 1 along the preset path, ensuring that the UAV 36 can accurately reach the reconnaissance area and provide guarantee for road condition monitoring. The flight attitude control accurately controls the attitude of the UAV 36 during the flight so that it can fly stably in front of the trailer 1 without being disturbed by external airflow factors, thereby ensuring the accuracy and stability of the collected images and data. The synchronous flight control controls the UAV 36 to maintain a speed synchronized with the trailer 1, so that the UAV 36 can continuously monitor the road conditions in front of the trailer 1 in real time.

[0047] Specifically, during the flight of the UAV 36, the flight attitude control submodule will continuously perform attitude adjustment operations to ensure that the UAV can always maintain a stable flight attitude. Even if it encounters interference from external airflow factors, such as sudden crosswinds, updrafts or downdrafts, the submodule can quickly sense attitude changes and make timely adjustments, so that the UAV can return to a stable attitude in the shortest time; the synchronous flight control submodule must first achieve speed matching between the UAV and the trailer. It can obtain the driving speed information of the trailer 1 from the vehicle control system through data exchange with the on-board central control system, and adjust the flight speed of the UAV 36 to a speed that is synchronized with the trailer 1. In this process, the submodule will continuously monitor the difference between the actual flight speed of the UAV 36 and the speed of the trailer, measure it through the airspeed meter of the UAV 36, and compare and analyze the measurement results (since the transportation of the pipeline corridor 5 is carried out by trailer 1, the speed of trailer 1 is generally around 60 kilometers per hour, and the DJI Air 3 UAV 36 meets the synchronous flight speed matching).

[0048] In this embodiment, the vehicle control system includes a vehicle control module, a wireless charging docking module, a weather detection module, a wireless transmission module and a data processing module. The vehicle control module ensures that the vehicle can stably drive according to the driver's strategy by accurately controlling the acceleration, deceleration, steering and braking operations of the vehicle. The wireless charging docking module accurately determines the position and posture of the drone 36 relative to the front of the trailer 1 by using positioning technology and sensors, guides the drone 36 to accurately land at the charging position on the top of the trailer 1 and establishes a stable charging connection at the same time. The weather detection module collects weather data around the vehicle in real time through various meteorological sensors installed on the vehicle, analyzes and processes the collected weather data, and promptly sends warning information to the driver when it detects that severe weather conditions or weather change trends may affect the driving safety of the vehicle. The wireless transmission module overcomes interference and signal attenuation problems in wireless communication by automatically selecting the optimal communication frequency band, adjusting the transmission power and adopting error correction coding technology to ensure efficient and accurate transmission of data between the trailer 1 and the external device. By automatically selecting the optimal communication frequency band, adjusting the transmission power, adopting error correction coding and other technical means, the interference and signal attenuation problems in wireless communication are overcome to ensure efficient and accurate transmission of data between the vehicle and the external device. At the same time, ensure the security of communication and prevent data from being illegally stolen or tampered with, data processing module.

[0049] In this embodiment, the steps for the drone 36 to calculate the road bumpiness by using a texture analysis algorithm based on a gray level co-occurrence matrix are as follows:

[0050] Feature extraction: using a drone to collect road condition photos within a certain distance in front of the trailer, the drone 36 is set within a range of 50 to 100 meters in front of the trailer, and takes photos at intervals of 5 meters, and the photo height is kept relatively stable between 5 and 10 meters;

[0051] After graying these photos, their grayscale co-occurrence matrix is ​​calculated. Common grayscale calculation formulas are as follows: Gray = 0.299*R+0.587*G+0.014*B, where Gray represents the grayscale value, and R, G, and B represent the red, green, and blue component values ​​of the pixels in the original color image, respectively. Through grayscale processing, the amount of image data can be reduced, the texture features of the image can be highlighted, and it is more convenient to calculate the grayscale co-occurrence matrix in the subsequent process. Based on the grayscale image, appropriate parameters are selected to calculate the grayscale co-occurrence matrix. Generally, it is necessary to determine the direction and distance of the calculation (usually in pixels, and the appropriate value is selected according to the image resolution and the actual road conditions. For example, you can try 1 pixel, 2 pixels, etc.). Then, according to the selected direction and distance, count the number of times different grayscale pixels in the image appear at a specified distance in a specific direction, so as to construct the grayscale co-occurrence matrix;

[0052] Next, texture features such as contrast, correlation, energy and entropy are extracted from the gray-level co-occurrence matrix. Contrast reflects the drastic change of grayscale values ​​in the image. For bumpy roads, the contrast value is usually higher; correlation reflects the linear relationship between pixels, and the correlation is higher for flat roads; energy indicates the uniformity of image grayscale distribution, and the energy is lower for bumpy roads with complex textures; entropy reflects the amount of information in the image, and the entropy value of bumpy areas is generally larger; Contrast: calculated by a specific formula, it reflects the drastic change of grayscale values ​​in the image, and the calculation formula is generally: Contrast = ∑∑(ij) 2 *P(i,j), where i and j represent gray levels. P(i,j) represents the number of occurrences of pixel combinations with gray levels i and j in the gray level co-occurrence matrix. For bumpy roads, the gray value changes significantly due to potholes and bumps on the road surface, so the contrast value is usually higher.

[0053] Correlation: reflects the linear relationship between pixels. For example, the calculation formula is: Correlation = ∑∑[(i-μi)(j-μj)*P(i,j)] / (σi*σj), where μi and μj are the means of gray levels i and j, respectively, and σi and σj are the standard deviations of gray levels i and j, respectively. Under flat road conditions, the gray value changes between pixels are relatively small, and the correlation is high.

[0054] Energy: Indicates the uniformity of the grayscale distribution of the image. The calculation formula is: Energy = ∑∑P(i,j) 2 ,The bumpy road conditions with complex texture will make the gray level distribution uneven and ,lower energy;

[0055] Entropy: The calculation formula is as follows: Entropy = -∑∑P(i,j*log2P(i,j). The entropy value of the bumpy area is generally larger due to its complex texture and grayscale changes.

[0056] Establish a bumpiness assessment model, collect a large number of drone photos under different road conditions and their corresponding actual bumpiness assessment data, which are obtained through professional measurement equipment or manual field surveys. The calculated texture features are used as input and the actual bumpiness is used as output. The assessment model is established using the support vector machine algorithm.

[0057] A speed decision model is established, the input of which is the road bumpiness, and the output is the most suitable driving speed of the trailer. Through a large amount of experimental data and actual driving experience, the mapping relationship between different bumpiness and the most suitable speed is determined. Through a piecewise function, the road bumpiness is divided into several levels: flat (bumpiness value is 0-0.2), slightly bumpy (bumpiness value is 0.2-0.5), moderately bumpy (bumpiness value is 0.2-0.5), moderately bumpy (bumpiness value is 0.5-0.8) and severely bumpy (bumpiness value is 0.8-1). For flat road conditions, the speed decision model may output the most suitable speed of 80-100 km / h; for slightly bumpy road conditions, the output speed is 60-80 km / h; for moderately bumpy road conditions, the output speed is 40-60 km / h; for severely bumpy road conditions, the output speed is 20-40 km / h;

[0058] Calculation and speed adjustment, calculate the texture features of the current road conditions, and then input them into the trained bumpiness assessment model to obtain the bumpiness assessment value of the current road conditions, input the bumpiness assessment value into the speed decision model, calculate the most appropriate driving speed of trailer 1, and the on-board central control system sends instructions to the vehicle control system based on the calculation results to automatically adjust the driving speed of trailer 1 to keep it within the most appropriate speed range.

[0059] The information is displayed on the display screen of the vehicle's central control system.

[0060] In this embodiment, a fixing plate 2 is provided on the top of the front of the trailer 1, a limiting groove is provided on the body of the fixing plate 2, a charging mechanism 3 is placed inside the limiting groove, limiting blocks 4 are symmetrically provided on the surface of the compartment of the trailer 1, threaded holes are provided on the inclined surfaces of the limiting blocks 4, the inclined surfaces of the limiting blocks 4 are fixedly connected to the surface of the compartment of the trailer 1 by threads, right-angle frames 6 are fixedly connected to the opposite surfaces of the two limiting blocks 4, a pipe gallery 5 is provided between the two limiting blocks 4, and the four corners of the lower end of the pipe gallery 5 are in contact with the surface of the right-angle frame 6 respectively.

[0061] Specifically, the fixing plate 2 is placed on the top of the front of the trailer 1, which is in a relatively conspicuous and critical position. Its main function is to provide a stable placement foundation for the charging mechanism 3, ensuring that the charging mechanism 3 can maintain a relatively fixed position even when encountering bumps, shaking, etc. during the driving of the vehicle, thereby providing reliable conditions for subsequent charging and docking operations of the drone. When the corridor 5 is placed between the two limit blocks 4, the right-angle frame 6 can provide support force to the four corners of the lower end of the corridor 5 from below, so that the corridor 5 can maintain a relatively stable placement state on the car body, reducing the shaking and displacement of the corridor 5 during vehicle transportation, thereby ensuring the transportation safety and integrity of the corridor 5.

[0062] In this embodiment, the charging mechanism 3 includes a plurality of cylinders 31. The four sides of the fixing plate 2 are respectively provided with mounting grooves. The cylinder body of the cylinder 31 is fixedly connected to the inner wall of the mounting groove. One end of the piston rod of the cylinder 31 is fixedly connected to a push rod 32. The push rods 32 are divided into two types, long and short, and are opposite to each other. The length of the short push rod 32 is the same as the width of the wireless charging plate 37. When the two long push rods 32 push the wireless charging plate 37 to the middle position, the two short push rods 32 are adjusted for the second time to push it to the axial position of the mounting groove.

[0063] Both ends of the push rod 32 are slidably connected to the inner wall of the mounting groove, and a stepper motor 33 is arranged at the upper end of the push rod 32. One end of the output shaft of the stepper motor 33 is fixedly connected to a connecting rod, and both ends of the connecting rod are rotatably connected to a pressing block 34. The design of the above structure is convenient for secondary fixing of the wireless charging plate 37 after it has been positioned, so as to avoid the vibration and shaking generated by the trailer 1 during driving to affect the contact between the wireless charging plate 37 and the power supply plate, thereby ensuring the wireless charging effect between the wireless charging plate 37 and the power supply plate;

[0064] A sensor 35 is provided between the two pressing blocks 34, and the sensor 35 is used to locate whether the wireless charging plate 37 is located at the axis of the installation groove;

[0065] The lower end of the sensor 35 is fixedly connected to the upper surface of the push rod 32, and a power supply board is provided on the bottom wall of the installation groove opened on the fixing plate 2. An infinite wireless charging board 37 is attached to the upper surface of the power supply board. The side of the wireless charging board 37 is fixedly connected to the lower end of the drone 36 through a connecting column, and the upper surface of the wireless charging board 37 is tightly attached to the lower surface of the pressing block 34.

[0066] In this embodiment, a silicone gasket is disposed on the lower surface of each pressing block 34 .

[0067] Specifically, the silicone gasket is used to increase the friction between the pressing block 34 and the wireless charging plate 37 , thereby increasing the fixing effect and preventing the wireless charging plate 37 from shaking while causing the drone 36 to shake, thereby preventing the drone 36 from being damaged.

[0068] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A prefabricated integrated pipe gallery transport trailer early warning system, characterized in that: The steps are as follows: Step 1: When it is necessary to conduct reconnaissance of the road conditions ahead, the driver starts the drone control system through the vehicle-mounted central control system, inputs the target address into the Beidou navigation system for navigation, and at the same time, the Beidou navigation system synchronizes the navigation path to the communication module set in the drone control system in the standby state. After receiving the navigation path, the drone control system performs path analysis and planning, and then lets the drone (36) take off to conduct the reconnaissance mission. The drone (36) flies to the front position of the trailer (1) head with a preset distance between the two according to the pre-synchronized navigation path and real-time road condition information, and transmits the detected images and data back to the vehicle-mounted central control system in real time through the communication module. The bumpiness of the road conditions ahead is calculated through an algorithm, and then the bumpiness is classified and analyzed, and then the driver is informed through the vehicle-mounted central control system that he needs to reduce the speed to a specified speed to pass; Step 2: The drone (36) located in front of the front of the trailer (1) flies to a preset distance from the front of the trailer (1), and maintains a speed that is synchronized with the trailer (1). The power management and monitoring system set in the drone control system synchronously monitors the power of the drone (36). If the battery power is lower than the preset value, the vehicle-mounted central control system sends a warning message to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to the communication module of the drone control system to recover the charging. The drone control system dispatches this signal to its internal task execution and control module, and the task execution and control module controls the drone (36) to fly back to the top of the charging position of the trailer (1). Before charging, the drone (36) feeds back the charging signal to the vehicle-mounted central control system, and the vehicle-mounted central control system sends a signal to the drone control system to start another drone (36) to continue to perform the road condition monitoring task; In step 3, after the drone (36) that returns to charge in step 2 falls into the charging position on the roof of the trailer (1), the unmanned charging docking module in the vehicle control system uses a wireless charging system based on an orthogonal magnetic coupling mechanism to send a charging signal to the charging mechanism (3) arranged on the top of the trailer (1).

2. According to claim 1, a prefabricated integrated pipe gallery transport trailer early warning system is characterized by: The vehicle-mounted central control includes a Beidou navigation system, a vehicle control system and an unmanned aerial vehicle control system. The vehicle control system collects vehicle operation status information through various sensors and accurately controls various components of the vehicle according to a preset control strategy. The unmanned aerial vehicle control system is responsible for two-way communication with the unmanned aerial vehicle (36) and comprehensive management of its flight operations.

3. According to claim 2, a prefabricated integrated pipe gallery transport trailer early warning system is characterized in that: The drone control system includes a power management and monitoring module, a task execution and control module, a drone switching management module, and a data transmission and processing module. The power management and monitoring module includes power monitoring and charging scheduling. The power monitoring monitors the power of the drone in real time. When the power is lower than a preset value, it promptly sends an early warning message to the vehicle-mounted central control system to remind the system that the drone needs to be charged or replaced to ensure that the drone can continue to perform the road condition monitoring task. The charging scheduling receives the recovery charging signal sent by the vehicle-mounted central control system and dispatches the signal to the task execution and control module to control the drone to fly back to the top of the charging position of the trailer (1).

4. The prefabricated integrated pipe gallery transport trailer early warning system according to claim 3 is characterized by: The task execution and control module includes path planning and navigation, flight attitude control and synchronous flight control. The path planning and navigation receives the navigation path synchronized with the Beidou navigation system, performs path analysis and planning, and guides the drone (36) to fly to a designated position in front of the front of the trailer (1) according to a preset path, thereby ensuring that the drone (36) can accurately reach the reconnaissance area and provide guarantee for road condition monitoring. The flight attitude control accurately controls the attitude of the drone (36) during flight so that it can stably fly in front of the trailer (1) without being disturbed by external airflow factors, thereby ensuring the accuracy and stability of the collected images and data. The synchronous flight control controls the drone (36) to maintain a speed synchronized with the trailer (1), thereby enabling the drone (36) to continuously monitor the road condition in front of the trailer (1) in real time.

5. The prefabricated integrated pipe gallery transport trailer early warning system according to claim 4 is characterized by: The vehicle control system comprises a vehicle control module, a wireless charging docking module, a weather detection module, a wireless transmission module and a data processing module. The vehicle control module ensures that the vehicle can stably travel according to the driver's strategy by accurately controlling the acceleration, deceleration, steering and braking operations of the vehicle. The wireless charging docking module accurately determines the position and posture of the drone (36) relative to the front of the trailer (1) by using positioning technology and sensors, guides the drone (36) to accurately land at the charging position on the top of the trailer (1) and establishes a stable charging connection. The weather detection module collects weather data around the vehicle in real time through various meteorological sensors installed on the vehicle, and analyzes the collected weather data. Analysis and processing, when it is detected that severe weather conditions or weather change trends may affect the driving safety of the vehicle, a warning message is promptly issued to the driver. The wireless transmission module overcomes the interference and signal attenuation problems in wireless communication by automatically selecting the optimal communication frequency band, adjusting the transmission power and adopting error correction coding technology, ensuring efficient and accurate transmission of data between the trailer (1) and the external device. By automatically selecting the optimal communication frequency band, adjusting the transmission power and adopting error correction coding technology, the interference and signal attenuation problems in wireless communication are overcome, ensuring efficient and accurate transmission of data between the vehicle and the external device. At the same time, the security of communication is guaranteed to prevent data from being illegally stolen or tampered with. The data processing module.

6. The prefabricated integrated pipe gallery transport trailer early warning system according to claim 5 is characterized by: The steps of calculating the road bumpiness by the drone (36) using a texture analysis algorithm based on a gray-level co-occurrence matrix are as follows: Feature extraction, first, the drone is used to collect road condition photos within a certain distance in front of the trailer. After graying these photos, the gray level co-occurrence matrix is ​​calculated. Then, the contrast, correlation, energy and entropy texture features are extracted from the gray level co-occurrence matrix. The contrast reflects the drastic change of the gray value in the image. For bumpy road conditions, the contrast value is usually higher; the correlation reflects the linear relationship between pixels, and the correlation is higher for flat road conditions; the energy indicates the uniformity of the gray distribution of the image, and the energy of bumpy road conditions with complex textures is lower; the entropy reflects the amount of information in the image, and the entropy value of the bumpy area is generally larger; Establish a bumpiness assessment model, collect a large number of drone photos under different road conditions and their corresponding actual bumpiness assessment data. These data are obtained through professional measurement equipment or manual field surveys. The calculated texture features are used as input and the actual bumpiness is used as output. Use machine learning algorithms to establish an assessment model. A speed decision model is established. The input of the model is the road bumpiness, and the output is the most suitable driving speed for the trailer. Through a large amount of experimental data and actual driving experience, the mapping relationship between different bumpiness and the most suitable speed is determined; Calculate and adjust the speed, calculate the texture features of the current road condition, and then input it into the trained bumpiness assessment model to obtain the bumpiness assessment value of the current road condition, and input the bumpiness assessment value into the speed decision model to calculate the most appropriate driving speed of the trailer (1); The information is displayed on the display screen of the vehicle's central control system.

7. The prefabricated integrated pipe gallery transport trailer warning system according to claim 6 is characterized by: A fixing plate (2) is arranged on the top of the front of the trailer (1), a limiting groove is provided on the body of the fixing plate (2), the charging mechanism (3) is placed inside the limiting groove, limiting blocks (4) are symmetrically arranged on the surface of the compartment of the trailer (1), threaded holes are provided on the inclined surfaces of the limiting blocks (4), the inclined surfaces of the limiting blocks (4) are fixedly connected to the surface of the compartment of the trailer (1) by threads, a right-angle frame (6) is fixedly connected to the opposite surfaces of the two limiting blocks (4), a pipe gallery (5) is arranged between the two limiting blocks (4), and the four corners of the lower end of the pipe gallery (5) are respectively in contact with the surface of the right-angle frame (6).

8. The prefabricated integrated pipe gallery transport trailer warning system according to claim 7 is characterized by: The charging mechanism (3) comprises a plurality of cylinders (31), the four sides of the fixing plate (2) are respectively provided with mounting grooves, the cylinder body of the cylinder (31) is fixedly connected to the inner wall of the mounting groove, one end of the piston rod of the cylinder (31) is fixedly connected to a push rod (32), the two ends of the push rod (32) are slidably connected to the inner wall of the mounting groove, the upper end of the push rod (32) is provided with a stepping motor (33), one end of the output shaft of the stepping motor (33) is fixedly connected to a connecting rod, and the two ends of the connecting rod are rotatably connected to a pressing rod. Block (34), a sensor (35) is arranged between the two pressing blocks (34), the lower end of the sensor (35) is fixedly connected to the upper surface of the push rod (32), a power supply board is arranged on the bottom wall of the installation groove opened on the fixing plate (2), the upper surface of the power supply board is attached to an unlimited wireless charging board (37), the side of the wireless charging board (37) is fixedly connected to the lower end of the drone (36) through a connecting column, and the upper surface of the wireless charging board (37) is tightly attached to the lower surface of the pressing block (34).

9. The prefabricated integrated pipe gallery transport trailer early warning system according to claim 8 is characterized by: A silicone gasket is provided on the lower surface of each pressing block (34).

Images